Archive for ISBA
“Prior selection is the fundamental issue in Bayesian statistics. Priors are the Bayesian’s greatest tool, but they are also the greatest point for criticism: the arbitrariness of prior selection procedures and the lack of realistic sensitivity analysis (…) are a serious argument against current Bayesian practice.” (p.23)
A paper that I first read and annotated in the very early hours of the morning in Banff, when temperatures were down in the mid minus 20′s now appeared on arXiv, “Penalising model component complexity: A principled, practical approach to constructing priors” by Thiago Martins, Dan Simpson, Andrea Riebler, Håvard Rue, and Sigrunn Sørbye. It is a highly timely and pertinent paper on the selection of default priors! Which shows that the field of “objective” Bayes is still full of open problems and significant advances and makes a great argument for the future president [that I am] of the O’Bayes section of ISBA to encourage young Bayesian researchers to consider this branch of the field.
“On the other end of the hunt for the holy grail, “objective” priors are data-dependent and are not uniformly accepted among Bayesians on philosophical grounds.” (p.2)
Apart from the above quote, as objective priors are not data-dependent! (this is presumably a typo, used instead of model-dependent), I like very much the introduction (appreciating the reference to the very recent Kamary (2014) that just got rejected by TAS for quoting my blog post way too much… and that we jointly resubmitted to Statistics and Computing). Maybe missing the alternative solution of going hierarchical as far as needed and ending up with default priors [at the top of the ladder]. And not discussing the difficulty in specifying the sensitivity of weakly informative priors.
“Most model components can be naturally regarded as a flexible version of a base model.” (p.3)
The starting point for the modelling is the base model. How easy is it to define this base model? Does it [always?] translate into a null hypothesis formulation? Is there an automated derivation? I assume this somewhat follows from the “block” idea that I do like but how generic is model construction by blocks?
“Occam’s razor is the principle of parsimony, for which simpler model formulations should be preferred until there is enough support for a more complex model.” (p.4)
I also like this idea of putting a prior on the distance from the base! Even more because it is parameterisation invariant (at least at the hyperparameter level). (This vaguely reminded me of a paper we wrote with George a while ago replacing tests with distance evaluations.) And because it gives a definitive meaning to Occam’s razor. However, unless the hyperparameter ξ is one-dimensional this does not define a prior on ξ per se. I equally like Eqn (2) as it shows how the base constraint takes one away from Jeffrey’s prior. Plus, if one takes the Kullback as an intrinsic loss function, this also sounds related to Holmes’s and Walker’s substitute loss pseudopriors, no? Now, eqn (2) does not sound right in the general case. Unless one implicitly takes a uniform prior on the Kullback sphere of radius d? There is a feeling of one-d-ness in the description of the paper (at least till page 6) and I wanted to see how it extends to models with many (≥2) hyperparameters. Until I reached Section 6 where the authors state exactly that! There is also a potential difficulty in that d(ξ) cannot be computed in a general setting. (Assuming that d(ξ) has a non-vanishing Jacobian as on page 19 sounds rather unrealistic.) Still about Section 6, handling reference priors on correlation matrices is a major endeavour, which should produce a steady flow of followers..!
“The current practice of prior specification is, to be honest, not in a good shape. While there has been a strong growth of Bayesian analysis in science, the research field of “practical prior specification” has been left behind.” (*p.23)
There are still quantities to specify and calibrate in the PC priors, which may actually be deemed a good thing by Bayesians (and some modellers). But overall I think this paper and its message constitute a terrific step for Bayesian statistics and I hope the paper can make it to a major journal.
Scott Schmidler, Steve Scott and myself just submitted a proposal for holding the next World ISBA Conference in 2016 in Banff, Canada! After enjoying the superb environment of the Advanced in Scalable Bayesian computation workshop last week, we thought it would be worth a try as a potential location for the next meeting, esp. when considering the superlative infrastructure of the Banff Centre (meaning we really do not have to be local to be local organisers!), the very reasonable rates for renting the site and securing two hundred rooms, the potential for a special collaboration with BIRS, the scarcity of alternative proposals (as far as I can fathom) and the ultimate mountain environment… I remember fondly the IMS annual meeting of 2002 there, with a great special lecture by Hans Künsch and, exceptionally, an RSS Read Paper by Steve Brooks, Paulo Guidici and Gareth Roberts. (Not mentioning en exhilarating solo scramble up Mount Temple and another one with Arnaud Guillin up the chimneys of Mount Edith!) Since the deadline was this Saturday, March 15, we should hear pretty soon if we are successful in this bid. (Good luck to our Scottish friends from Edinburgh for their bid for holding ISBA 2018! Moving from the feet of Mount Rundle [above] to the feet of Arthur’s Seat would make for a great transition.)
After ABC in Paris in 2009, ABC in London in 2011, and ABC in Roma last year, things are accelerating since there will be—as I just learned— an ABC in Sydney next July (not June as I originally typed, thanks Robin!). The workshop on the current developments of ABC methodology thus leaves Europe to go down-under and to take advantage of the IMS Meeting in Sydney on July 7-10, 2014. Hopefully, “ABC in…” will continue its tour of European capitals in 2015! To keep up with an unbroken sequence of free workshops, Scott Sisson has managed to find support so that attendance is free of charge (free as in “no registration fee at all”!) but you do need to register as space is limited. While I would love to visit UNSW and Sydney once again and attend the workshop, I will not, getting ready for Cancún and our ABC short course there.
[Here is a call from the BayesComp Board for proposals for earlier poll on the ‘Og helped shape the proposal, with the year, 2016 vs. 2017, remaining open. I just added town to resort below as it did not sound from the poll people were terribly interested in resorts.]
The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship conference:
Bayesian Computing at MCMSki
The expectation is that the meeting will be held in January 2016, but the committee will consider proposals for other times through January 2017.
This meeting will be the next incarnation of the popular MCMSki series that addresses recent advances in the theory and application of Bayesian computational methods such as MCMC, all in the context of a world-class ski resort/town. While past meetings have taken place in the Alps and the Rocky Mountains, we encourage applications from any venue that could support MCMSki. A three-day meeting is planned, perhaps with an additional day or two of satellite meetings and/or short courses.
One page proposals should address feasibility of hosting the meeting including
1. Proposed dates.
2. Transportation for international participants (both the proximity of international airports and transportation to/from the venue).
3. The conference facilities.
4. The availability and cost of hotels, including low cost options.
5. The proposed local organizing committee and their collective experience organizing international meetings.
6. Expected or promised contributions from the host organization, host country, or industrial partners towards the cost of running the meetings.
Proposals should be submitted to David van Dyk (dvandyk, BayesComp Program Chair) at imperial.ac.uk no later than May 31, 2014.
The Board of Bayesian Computing Section will evaluate the proposals, choose a venue, and appoint the Program Committee for Bayesian Computing at MCMSki.